R Notebook

library(tidyverse)
library(reshape2)
library(lubridate)
library(forcats)
library(vistime)

temp_illustration <- data.frame(bucket = c("x < 62.8", "62.8 <= x < 71.3", "71.3 <= x < 79.8", "79.8 >= x > 85", "x >= 85"),
                                name = c("xcool_days", "cool_days", "normal_days", "hot_days", "xhot_days"),
                                start = c("32-01-01", "62-09-18", "71-03-18", "79-09-18", "85-01-01"),
                                end = c("62-09-18", "71-03-18", "79-09-18", "85-01-01", "100-01-01"),
                                color = c("cyan", "blue", "yellow", "orange", "red"),
                                optimize_y = TRUE)


vistime(temp_illustration, groups = "name", events = "bucket", title = "Temperature buckets")

temps_df %>%
  group_by(ride_name) %>%
  ggplot() +
  geom_col(aes(x = ride_name, y = mean_wait, fill = fct_relevel(temp_cat, temp_list_names)), position = "dodge") +
  scale_fill_manual(values = temp_colors) +
  coord_flip() +
  labs(fill = "Temp cat") +
  theme(legend.justification=c(1,1), legend.position=c(1,1)) +
  labs(title = "Mean estimated wait time for each ride at a temperature", fill = "Temperature") +
  ylab("Mean wait (minutes)") +
  xlab("Ride")

temps_df %>%
  inner_join(ride_metadata) %>%
  filter(splash == TRUE) %>%
  group_by(ride_name) %>%
  ggplot() +
  geom_col(aes(x = ride_name, y = mean_wait, fill = fct_relevel(temp_cat, temp_list_names)), position = "dodge") +
  scale_fill_manual(values = temp_colors) +
  coord_flip() +
  labs(fill = "Temp cat") +
  theme(legend.justification=c(1,1), legend.position=c(1,1)) +
  labs(title = "Mean estimated wait for rides with a splash feature at a temperature", fill = "Temperature") +
  ylab("Mean wait (minutes)") +
  xlab("Ride")

Joining, by = "ride_name"
Column `ride_name` joining factors with different levels, coercing to character vector

temps_df %>%
  inner_join(ride_metadata) %>%
  filter(indoor == TRUE) %>%
  group_by(ride_name) %>%
  ggplot() +
  geom_col(aes(x = ride_name, y = mean_wait, fill = fct_relevel(temp_cat, temp_list_names)), position = "dodge") +
  scale_fill_manual(values = temp_colors) +
  coord_flip() +
  labs(fill = "Temp cat") +
  theme(legend.justification=c(1,1), legend.position=c(1,1)) +
  labs(title = "Mean estimated wait time for indoor rides at a temperature", fill = "Temperature") +
  ylab("Mean wait (minutes)") +

  xlab("Ride")

Joining, by = "ride_name"
Column `ride_name` joining factors with different levels, coercing to character vector

temps_df %>%
  inner_join(ride_metadata) %>%
  filter(splash == TRUE & indoor == TRUE) %>%
  group_by(ride_name) %>%
  ggplot() +
  geom_col(aes(x = ride_name, y = mean_wait, fill = fct_relevel(temp_cat, temp_list_names)), position = "dodge") +
  scale_fill_manual(values = temp_colors) +
  coord_flip() +
  labs(fill = "Temp cat") +
  theme(legend.justification=c(1,1), legend.position=c(1,1)) +
  labs(title = "Mean estimated wait time for indoor + splash rides at a temperature", fill = "Temperature") +
  ylab("Mean wait (minutes)") +
  xlab("Ride")